multi-disciplinary optimisation for planetary entry, descent and landing systems

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- 1 -

2nd UK Workshop on Optimisation in Space Engineering

Cambridge, 19 Mar 2014© 2014 Deimos Space UK Ltd. - www.deimos-space.com

Multi-Disciplinary Optimisation for Planetary Entry, Descent and

Landing Systems

David RileyDeimos Space Ltd., UK

Davide BonettiDeimos Space S.L.U., Spain

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Table of Contents

• Introduction: The Problem Domain

• The Optimisation Process

• Tools for Numerical Trade-offs

• Optimization results

• Conclusion

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- 3 -



INTRODUCTION:

THE PROBLEM DOMAIN

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The problem

• Top-level requirement

– Bring a spacecraft safely to rest on the surface of another planet

• Challenges: Mars

– Entry velocity ~ 3 km/s

– Very thin atmosphere• equivalent to 35 km altitude on Earth

– Gravity around 1/3 of Earth’s

– Want to maximise the payload delivered

to the surface

– Subject to constraints in cost, risk,

geographical sourcing, development

timescale…

Image: NASA/JPL

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Entry, Descent and Landing Systems

• Entry: aerodynamic deceleration using a heatshield

• Deploy a parachute to slow down faster

• Retro-rockets for final slowdown and fine control

• Airbags for touchdown

Entry Descent A Descent B Airbag inflation Free fall

FS Release RA Lowering Powered Descent Touchdown

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Entry, Descent and Landing Systems

• Some parts are common:

– Heatshield for entry

– Parachute for deceleration





Entry Descent A Descent B Descent C Airbag inflation

FS Release Free fall Touchdown

• Others vary:

– One vs two parachute stages

– Whether to have a retro stage

– Touchdown on airbags, landing

legs, crushables…

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Driving requirements

• Mass

• Height loss and verticalisation

• Thermo-mechanical loads

• Landing site accuracy

• Volume

• Reliability / robustness

• Cost

• Development timescale

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Trade-offs

Smaller parachute / bigger retros:

– Lighter parachute system

– Final speed is higher

– More retro fuel required



Single-stage vs two-stage parachute:

– For low final speeds, two-stage

generally is lower mass

– Two-stage may take more

altitude to reach final speed

– Single-stage is simpler

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Optimisation problem

• Problem includes discrete choices, integer values and continuous values

• Multi-disciplinary approach required

– Parachute design, retro system design, airbag design, trajectory, …

• Can’t split the problem into separate pieces - different parts of the design

affect each other

– Speed reached under heatshield must be safe for parachute deployment

– Each stage must carry the subsequent stages• e.g. parachute must slow down the retro fuel and airbags as well as the payload

• Need the trajectory to be self-consistent

– Test through simulation: enough time and altitude to stop before hitting ground





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High Precision Landers: overview

• Mission concept

– Technology Research Study within the ESA MREP

(Mars Robotic Exploration Preparatory) Programme,

preliminary to Mars Sample Return

– Rover and return vehicle must land near each other

– Target is launch in late 2020’s

• Main study objective

– Design an optimum and robust EDL/GNC

configuration for MSR lander

– Project led by Airbus Defence & Space (Les Mureaux)

• Main requirements

– Achieve landing accuracy of at least 10 km, ideally

high precision (3 km) or even pinpoint (100 m)

– Mass at entry ~ 800 kg

– Use European technology as far as possibleImages: NASA/JPL

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Small Mars Landers: overview

• Mission concept

– Technology Research Study within the ESA MREP

(Mars Robotic Exploration Preparatory) Programme,

preliminary to INSPIRE

– Target is launch in 2026 or 2028

• Main study objective

– Design an optimum and robust EDL/GNC

configuration for a Mars Network Science mission

– Project led by Deimos Space (Spain)

• Main requirements

– Three identical probes separating from the same

carrier and landing in different sites

– Each probe mass at entry ~350 kg

– Payload: 130 kg, 1150x355 mm (cylinder)

– Use European technology as far as possible

Image: ESA

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Small Mars Landers: overview

10-3

10-2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

4

Pth. 99.5% CI Up density (kg/m3)

altit

ude

(m)

Global Model

Elysium1Elysium2

Elysium3

One site

Global model• Challenges of a multi-probe mission

– Several studies, only one launched (crashed)

– Target entry mass quite different to previous

single or multiple landers solutions

• Wide environment variability

– Triple landing site, identical probes

– Altitude below 0 MOLA, -15º < Latitude < 30º

– Global atmosphere models based on statistics

of European Mars Climate Database calls

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THE OPTIMISATION PROCESS

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Overall Process

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Overall Process

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Preliminary EDL & GNC Trade-offsTRADE-OFFS SUMMARY

Ph Id Trade-off Options

Arrival

A1 Arrival conditions

Direct Escape GTO constrained GTO 2022 unconstrained GTO 2024 unconstrained

Separation &

Coasting

SC1 SED Militar/Civil Heritage Beagle-2 Cassini/Hyugens

SC2 Entry type Prograde Retrograde

SC3 Separation times ESAT dimensions [25:0.5] days before EIP

Entry

E1 Aeroshell concept

70º cone + sphere 60º cone + sphere IBD Parashield

E2 Nose Radius 0.25 diameters 0.45 diameters

E3 TPS material

SLA-561V Norcoat-Liege AQ60 / Prosial ALESTRASIL PICSIL DO31 / SPA ASTERM

E4 Entry Control system Spinning 3-axis RCS damping

E5 FPA ESAT dimension [-18:-10.5]ºE6 Capsule diameter ESAT dimension [1.4:2] mE7 Capsule backshell angle ESAT dimension [30:50]º

Descent and Landing

DL1 Parachutes type

DGB Ringslot Ringsail Hemisflo Cruciform

DL2Number of parachute stages

Single Two stage

DL3 Use of retrorockets

No retros Retro-assisted parachute phase Fire retros after parachute separation Skycrane

DL4 Controllability Fixed Thrust - non pulsed Fixed Thrust – pulsed Throttleable (variable thrust)

DL5Velocity reduction by means of thrust

None Only Vertical Only Horizontal Vertical and Horizontal

TRADE-OFFS SUMMARY Ph Id Trade-off Options

Descent and

Landing

DL6 Use of lowering system No Yes

DL7 Landing system

Legs Vented Airbags Non-Vented airbags Crushable (as stand-alone system, not part of legs or airbag system)

DL8Backshell avoidance manoeuvre

No Yes

DL9Parachute avoidance manoeuvre / flyaway

No

Yes

GNC

G1 Entry Guidance None Hypersonic guidance

G2 Descent Guidance None Guided parachute

G3 Landing Guidance Apollo derived G-turn None

N1 Coasting & Entry Navigation timer+MU timer+IMU+sun sensor

N2 Descent & Landing Navigation

timer+MU timer+RA timer+IMU+RA timer+IMU+Camera timer+IMU+Camera+RA timer+IMU+Camera+RD

C1 Entry Control None Liquid rockets

C2Descent & Landing velocity Control

None Solid rockets Liquid rockets

C3Descent & Landing attitude control

None Liquid rockets

T1 Descent triggering IMU-based correlation Navigated velocity Navigated dynamic pressure

T2 Main parachute triggering None Timer

T3 RA activation None Timer from mortar

T4 Retros triggering None Altitude and velocity

T5 Landing triggering None Altitude and velocity

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3 Configurations selected for ESAT numerical trade-offs Entry Descent A Descent B Airbag inflation Free fall






SIMPLE

ROBUST

TY

PE 1

TY

PE 2

TY

PE3

Sub-system TypeAeroshell 70º sphere-coneEntry BallisticDescent 2 stages (DGB+Ringsail)Retro-rockets NoneLowering NoneLanding Non vented airbagsGNC sensors Sun sensor, RA, IMU

Sub-system TypeAeroshell 70º sphere-coneEntry BallisticDescent 1 stage (DGB)Retro-rockets Solid (Vertical only)Lowering YesLanding Non vented airbagsGNC sensors Sun sensor, RA, IMU

Sub-system TypeAeroshell 70º sphere-coneEntry BallisticDescent 1 stage (DGB)Retro-rockets Solid (Vertical & Horizontal)Lowering YesLanding Non vented airbagsGNC sensors Sun sensor, RA, IMU, VBN

MER-LIKE

MPF-LIKE

Beagle2-LIKE(no lowering)

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Overall Process

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Overall Process

ESAT: EDL&GNC Sizing and Analysis Tool

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TOOLS FOR

NUMERICAL TRADE-OFFS

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- 21 -



ESAT Architecture

ESAT relies on a modular and generic infrastructure.

Users concentrate efforts on Settings and Wrapper Function to solve a given problem.

External module:- multidisciplinary- single discipline

EDL&GNC Sizing and AnalysisTool

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MDO Architecture

C: CoastingE: EntryD: DescentL: LandingP/L: PayloadQdyn: Dynamic pressureFS: FrontshieldVt: terminal VNSP: Network Science Probe

Different forType 1, 2 and 3

SIM

ULA

TIO

N C

OR

E

Overall it is a complex problem:

– Multiple phases

– Multiple combinations of worst cases

(aerodynamics, atmosphere,

events…) => robust solutions

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Bi-level MDO Architecture

• Bi-level surrogate models with internal optimization

– Level1: MDO (mission & system objectives)

– Level2: Coasting, D&L (nested in level 1)

• Achieve efficient optimizations

PESDO (TESSELLA)

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OPTIMIZATION RESULTS

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Problem 1: Probe separation timing

• 3 probes, want to optimise the separation events

ESAT wrapper for Small Mars Landers

Coasting and separation overview

=> simplified ESAT example =>

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Problem 1: Probe separation timing

• Look at results for sites 1 and 3

Fuel mass for retargeting EIP FPA dispersionPareto Front: Dominant solutions

10

10

10

10

10

10

10

20

20

30

30

20

20

20

40

40

50

50

30

30

30

75

75

40

40

40

100100

50

50

50

30

75

75

Tsep Elysium 1 [days]

Tse

p E

lysi

um

3 [d

ays

]

Total Fuel Mass [kg]

5 10 15 20 25 30

5

10

15

20

25

30

10

20

30

40

50

60

70

80

90

100

0.60.6

0.6

0.6

0.6

0.6

0.6

0.6

0.8

0.8

0.8

0.8

0.8

0.8

0.8

0.8

11

1

11

1

1

1.2

1.2

1.2

1.2

1.2

1.2

1.4

1.4

1.4

1.4

1.4

1.4

1.6

1.6

1.6

1.6

1.6

1.8

1.8

1.8

1.8

1.8

2

2

2

2

2

2.22.2

2.2

2.2

1.21.41.6

1.82

2.2

Tsep Elysium 1 [days]

Tse

p E

lysi

um

3 [d

ays

]

FPA dispersion @EIP [deg]

5 10 15 20 25 30

5

10

15

20

25

30

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

0 10 20 30 40 50 60 70 80 90 1000

0.25

0.5

0.75

1

1.25

1.5

1.75

2

Total Fuel Mass

FP

A d

isp

ers

ion

@E

IP

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Problem 2: Minimisation of EDLS Mass

• EDL&GNC Robust Optimization Problem– 7 variable dimensions covering EDL, GNC and environment aspects

Type Phase DescriptionConstraint L Landing Accuracy E Maximum heat flux at nose E Maximum heat load at nose E Maximum dynamic pressure E/D Maximum load factor E/D Maximum dynamic pressure at parachute deployment

E/D Minimum altitude at parachute deployment E/D/L Percentage of non feasible simulations

C Total fuel mass for coasting phase E/D/L Consistency of volumes and sizesFixed E/D/L ESAT EDL/GNC configurationInputs L Payload mass L Payload diameter L Payload height E Capsule nose radius / Capsule diameter E Capsule front cone angle E Capsule shoulder radius / Capsule diameter E Capsule back plate diameter/capsule diameter E TPS density (Norcoat Liege)Variable ESAT Dimensions

E/D Capsule diameterE Capsule back cone angleE Flight Path Angle at EIP

E/D Dispersion on Mach number at parachute triggering D Dispersion on Altitude at Powered Descent Initiation D/L Dispersion on Altitude at Free Fall start

L Winds scale factor

Minimize system mass

Maximize altitude at parachute dep.

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• Configurations selection process: Query and Filtering– More than 100 performances have been

managed in 7 dimensions with ESAT

– 2D slices / N-dim data mining

– Statistics of 50000 different samples0 0.5 1 1.5 2

0

20

40

60

80

100

all checks [-]

Per

cent

ile

400 410 420 430 4400

20

40

60

80

100

EIP Mass [kg]

Per

cent

ile

700 800 900 10000

20

40

60

80

100

Max qdyn drogue dep. [Pa]

Per

cent

ile

62 64 66 68 700

20

40

60

80

100

All-up mass of D&L system [kg]

Per

cent

ile

0.8 1 1.2 1.4 1.6

x 105

0

20

40

60

80

100

End alongtrack disp (3sigma) [m]

Per

cent

ile

12.5 13 13.5 14 14.5 150

20

40

60

80

100

max load factor [gE]

Per

cent

ile

5 6 7 8

x 105

0

20

40

60

80

100

max convective heat flux [W/m2]

Per

cent

ile

2000 4000 6000 8000 100000

20

40

60

80

100

max dynamic pressure [Pa]

Per

cent

ile

3.5 4 4.5 5

x 107

0

20

40

60

80

100

max heat load [J/m2]

Per

cent

ile

4000 4500 5000 5500 60000

20

40

60

80

100

min altitude @ 1st parachute dep [m]

Per

cent

ile

-1 -0.5 0 0.5 10

20

40

60

80

100

Percentage of NaN, phase 3 [-]

Per

cent

ile

15 20 25 300

20

40

60

80

100

Total Fuel Mass [kg]

Per

cent

ile

0.1

0.2

0.3

0.3

0.4

0.4

0.5

0.5

0.6

0.6

0.7

0.7

0.8

0.8

0.9

0.9

1

1

0

Capsule diameter [m]

Bac

k co

ne a

ngle

[deg

]

All checks [-]

1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.535

40

45

50

55

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

400

400

410

410

420

430

440

450

460

470

Capsule diameter [m]

Bac

k co

ne a

ngle

[deg

]

EIP Mass [kg]

1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.535

40

45

50

55

400

410

420

430

440

450

460

470

TOOSMALLCAPSULE

TOOHEAVYCAPSULE

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• Pareto Frontier: optimum solutions, = chosen one– Predicted and validated Pareto frontiers match with very good accuracy

400 420 440 460 480 500 5200

1000

2000

3000

4000

5000

6000

EIP Mass [kg]

min

alti

tude

@ 1

st p

arac

hute

dep

. [m

]

Type 1Type 2Type 3

60% of max winds

50% of max winds

96% of max winds

435 440 445 450 455 460 465 4704000

4200

4400

4600

4800

5000

5200

EIP Mass [kg]

min

alti

tude

@ 1

st p

arac

hute

dep

. [m

]

Filtered query pointsPredicted Pareto FrontValidated Pareto Front


TYPE 2

(mass includes system margins)

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Optimum configurations comparison

• Overall summary

Criteria Type 1 Type 2 Type 3 Probe mass at EIP, kg 404 440 500 Probe diameter, m 2.038 2.045 2.032 Winds close to ground, m/s < 21.3 < 13 < 10.6 Reachable landing sites, % 96.2 63.7 47.6 Performances Coasting Good Good Good

Entry Good Good Poor

Descent Good Good Fail

Landing Good Good Fail

Relative GNC complexity High Mid Low Good candidate solution? Yes Yes No

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Payload mass Frontshield

Backshell Airbags

Parachutes Retro-rockets (RAD)

Thermal control Spin and ejection device

MLI Clevises, Brackets, Miscellaneous

GNC sensors Bioseal

Heatshield instrumentation

0 20 40 60 80 100 120 140

Payload mass Frontshield

BackshellParachutes

Retro-rockets (RAD+TIRS) Airbags

Spin and ejection device Clevises, Brackets, Miscellaneous

Thermal control MLI

GNC sensors Bioseal

Heatshield instrumentation

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0

Configurations selected

• Type 1&2: Mass budget and Sizes

Type 1:BC ~ 73 kg/m2

Type 2:BC ~ 79 kg/m2

(mass includes system margins)

P/L = 32.2% of Total

P/L = 29.6% of Total

Element Type 1 Type 2 UnitsPayload volume 0.369 0.369 m3

Capsule diameter 2.038 2.045 mCapsule front cone angle 70.00 70.00 degCapsule back cone angle 44.290 44.809 degCapsule back cover diameter 1.019 1.022

m

Internal volume 1.383 1.381 m3

Sensors volume 0.003 0.002 m3

Parachute diameter 13.366 13.573 mParachute volume (stowed) 0.074 0.076

m3

RCS volume 0.012 0.012 m3

RCS diameter 0.124 0.124 mAirbags volume (stowed) 0.048 0.129 m3

Airbags thickness (stowed) 0.014 0.035

m

DL volume 0.134 0.216 m3

Ballistic coefficient 72.8 78.7 Kg/m2

kg

kg

Type 1: winds < 21.3 m/s

Type 2: Winds < 13 m/s

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Configurations selected

• Type 1&2: GNC solutions

• Type 1: Modes– Events (blue)

– Sensors (grey)

– State vector (red)

Function Phase GNC solution Equipments

GUIDANCE EDL N/A N/A

NAVIGATIONInertial: Coasting & Entry

Ballistic mode Inertial kinematic

Sun sensor + IMU

Relative: Descent and Landing

Hybridized with lateral velocity estimation

RA + IMU + VBN

CONTROL

Entry N/A N/A

Velocity control: Descent and Landing

Vertical velocity control/reduction Lateral velocity

compensation/control

Solid rockets for vertical/lateral control

(RAD+TIRS)

Attitude control: Descent and Landing

N/A N/A

Only for Type 1 configuration

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CONCLUSIONS

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Conclusions: Mars Landers

• Identified optimal landing site sequences, configurations and component

trade-offs for the Small Mars Landers project

• Robustness is critical for a network Mars landers mission

• Higher GNC complexity is the price of adding flexibility to site selection

– Vision based navigation

– Lateral control

• ESAT allows the System, Mission and GNC engineers to perform high-

fidelity EDL-GNC architecture trade-offs relaying on high-fidelity and end-

to-end approach. It increases the reliability of the selected design solutions

with a reduced number of iteration loops (number and extent)

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Conclusions: Design Optimisation Tools

• We have found the approach adds a lot to EDLS optimisation

• Need to choose an appropriate level of optimisation based on the level of

fidelity of the model

– No point fine-tuning something that exists in the model, not in reality

• Surrogate modelling tools like ESAT are valuable when the models are

complicated, slow to run

• Multi-disciplinary approach is vital – we’ve got as far as we can with tuning

each component separately, choosing handover conditions by guesswork

• Mathematical optimisation is effectively “yet another discipline”

– Important to add appropriate support not just extra complication

• ESAT is a useable front-end to a sophisticated optimisation approach

– Allows the user to focus on creating the “wrapper function”

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Acknowledgements

• Deimos Space (Spain)

– Gabriele De Zaiacomo, Irene Pontijas Fuentes, Rodrigo Haya Ramos,

Gabriele Bellei, Jordi Freixa Mallol

• Airbus Defence and Space

– Timothee Verwaerde, Aurélien Pisseloup, Cédric Renault

• European Space Agency

– Eric Bornschlegl, Kelly Geelen, Alvaro Martinez Barrio, Thomas Voirin

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Thank you!

multi-disciplinary optimisation for planetary entry, descent and landing systems

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